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Event-based Synthetic Aperture Imaging with a Hybrid Network

Event-based Synthetic Aperture Imaging with a Hybrid Network

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Zhang, Xiang Liao, Wei Yu, Lei Yang, Wen Xia, Gui-Song Wuhan Univ Wuhan Peoples R China

ISBN: (纸本)9781665445092

Synthetic aperture imaging (SAI) is able to achieve the see through effect by blurring out the off-focus foreground occlusions and reconstructing the in-focus occluded targets from multi-view images. However, very dense occlusions and extreme lighting conditions may bring significant disturbances to the SAI based on conventional frame-based cameras, leading to performance degeneration. To address these problems, we propose a novel SAI system based on the event camera which can produce asynchronous events with extremely low latency and high dynamic range. Thus, it can eliminate the interference of dense occlusions by measuring with almost continuous views, and simultaneously tackle the over/under exposure problems. To reconstruct the occluded targets, we propose a hybrid encoder-decoder network composed of spiking neural networks (SNNs) and convolutional neural networks (CNNs). In the hybrid network, the spatio-temporal information of the collected events is first encoded by SNN layers, and then transformed to the visual image of the occluded targets by a style-transfer CNN decoder. Through experiments, the proposed method shows remarkable performance in dealing with very dense occlusions and extreme lighting conditions, and high quality visual images can be reconstructed using pure event data.

关键词： Heuristic algorithms Lighting Dynamic range Apertures Cameras Visual effects pattern recognition

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BCNN: A Binary CNNWith All Matrix Ops Quantized To 1 Bit Precision

BCNN: A Binary CNNWith All Matrix Ops Quantized To 1 Bit Pre...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Redfern, Arthur J. Zhu, Lijun Newquist, Molly K. Texas Instruments Inc 12500 TI Blvd Dallas TX 75243 USA Georgia Inst Technol North Ave NW Atlanta GA 30332 USA

ISBN: (纸本)9781665448994

This paper describes a CNN where all CNN style 2D convolution operations that lower to matrix matrix multiplication are fully binary. The network is derived from a common building block structure that is consistent with a constructive proof outline showing that binary neural networks are universal function approximators. 71.24% top 1 accuracy on the 2012 ImageNet validation set was achieved with a 2 step training procedure and implementation strategies optimized for binary operands are provided.

关键词： Training computer vision Convolution conferences Neural networks Memory management Decoding

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Semantic hierarchies for visual object recognition

Semantic hierarchies for visual object recognition

引用

ieee conference on computer vision and pattern recognition

作者： Marszalek, Marcin Schmid, Cordelia LEAR LJK INRIA 665 Europe F-38330 Montbonnot St Martin France

ISBN: (纸本)9781424411795

In this paper we propose to use lexical semantic networks to extend the state-of-the-art object recognition techniques. We use the semantics of image labels to integrate prior knowledge about inter-class relationships into the visual appearance learning. We show how to build and train a semantic hierarchy of discriminative classifiers and how to use it to perform object detection. We evaluate how our approach influences the classification accuracy and speed on the PASCAL VOC challenge 2006 dataset, a set of challenging real-world images. We also demonstrate additional features that become available to object recognition due to the extension with semantic inference tools-we can classify high-level categories, such as animals, and we can train part detectors, for example a window detector, by pure inference in the semantic network.

关键词： Object recognition

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Query-Focused Video Summarization: Dataset, Evaluation, and A Memory Network Based Approach 30

Query-Focused Video Summarization: Dataset, Evaluation, and ...

引用

30th ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Sharghi, Aidean Laurel, Jacob S. Gong, Boqing Univ Cent Florida Ctr Res Comp Vis Orlando FL 32816 USA Univ Alabama Birmingham Dept Comp Sci Birmingham AL 35294 USA

ISBN: (纸本)9781538604571

Recent years have witnessed a resurgence of interest in video summarization. However, one of the main obstacles to the research on video summarization is the user subjectivity - users have various preferences over the summaries. The subjectiveness causes at least two problems. First, no single video summarizer fits all users unless it interacts with and adapts to the individual users. Second, it is very challenging to evaluate the performance of a video summarizer. To tackle the first problem, we explore the recently proposed query-focused video summarization which introduces user preferences in the form of text queries about the video into the summarization process. We propose a memory network parameterized sequential determinantal point process in order to attend the user query onto different video frames and shots. To address the second challenge, we contend that a good evaluation metric for video summarization should focus on the semantic information that humans can perceive rather than the visual features or temporal overlaps. To this end, we collect dense per-video-shot concept annotations, compile a new dataset, and suggest an efficient evaluation method defined upon the concept annotations. We conduct extensive experiments contrasting our video summarizer to existing ones and present detailed analyses about the dataset and the new evaluation method.

关键词： Semantics Measurement Visualization Automobiles Dictionaries computer vision Knowledge discovery Videotapes Dictionaries as Topic data mining Automobiles Semantics images computer vision Visualization evaluation method Dataset Cars semiotics

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Person Re-identification by Local Maximal Occurrence Representation and Metric Learning

Person Re-identification by Local Maximal Occurrence Represe...

引用

ieee conference on computer vision and pattern recognition (cvpr)

作者： Liao, Shengcai Hu, Yang Zhu, Xiangyu Li, Stan Z. Chinese Acad Sci Inst Automat Ctr Biometr & Secur Res Natl Lab Pattern Recognit 95 Zhongguancun East Rd Beijing 100190 Peoples R China

ISBN: (纸本)9781467369640

Person re-identification is an important technique towards automatic search of a person's presence in a surveillance video. Two fundamental problems are critical for person re-identification, feature representation and metric learning. An effective feature representation should be robust to illumination and viewpoint changes, and a discriminant metric should be learned to match various person images. In this paper, we propose an effective feature representation called Local Maximal Occurrence (LOMO), and a subspace and metric learning method called Cross-view Quadratic Discriminant Analysis (XQDA). The LOMO feature analyzes the horizontal occurrence of local features, and maximizes the occurrence to make a stable representation against viewpoint changes. Besides, to handle illumination variations, we apply the Retinex transform and a scale invariant texture operator. To learn a discriminant metric, we propose to learn a discriminant low dimensional subspace by cross-view quadratic discriminant analysis, and simultaneously, a QDA metric is learned on the derived subspace. We also present a practical computation method for XQDA, as well as its regularization. Experiments on four challenging person re-identification databases, VIPeR, QMUL GRID, CUHK Campus, and CUHK03, show that the proposed method improves the state-of-the-art rank-1 identification rates by 2.2%, 4.88%, 28.91%, and 31.55% on the four databases, respectively.

关键词： Discriminant analysis

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Learning Common Rationale to Improve Self-Supervised Representation for Fine-Grained Visual recognition Problems

Learning Common Rationale to Improve Self-Supervised Represe...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Shu, Yangyang van den Hengel, Anton Liu, Lingqiao Univ Adelaide Sch Comp Sci Adelaide SA Australia

ISBN: (纸本)9798350301298

Self-supervised learning (SSL) strategies have demonstrated remarkable performance in various recognition tasks. However, both our preliminary investigation and recent studies suggest that they may be less effective in learning representations for fine-grained visual recognition (FGVR) since many features helpful for optimizing SSL objectives are not suitable for characterizing the subtle differences in FGVR. To overcome this issue, we propose learning an additional screening mechanism to identify discriminative clues commonly seen across instances and classes, dubbed as common rationales in this paper. Intuitively, common rationales tend to correspond to the discriminative patterns from the key parts of foreground objects. We show that a common rationale detector can be learned by simply exploiting the GradCAM induced from the SSL objective without using any pre-trained object parts or saliency detectors, making it seamlessly to be integrated with the existing SSL process. Specifically, we fit the GradCAM with a branch with limited fitting capacity, which allows the branch to capture the common rationales and discard the less common discriminative patterns. At the test stage, the branch generates a set of spatial weights to selectively aggregate features representing an instance. Extensive experimental results on four visual tasks demonstrate that the proposed method can lead to a significant improvement in different evaluation settings.(1)

关键词： Self-supervised or unsupervised representation learning

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Joint Intensity and Spatial Metric Learning for Robust Gait recognition 30

Joint Intensity and Spatial Metric Learning for Robust Gait ...

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30th ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Makihara, Yasushi Suzuki, Atsuyuki Muramatsu, Daigo Li, Xiang Yagi, Yasushi Osaka Univ Suita Osaka 565 Japan Nanjing Univ Sci & Technol Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781538604571

This paper describes a joint intensity metric learning method to improve the robustness of gait recognition with silhouette-based descriptors such as gait energy images. Because existing methods often use the difference of image intensities between a matching pair (e.g., the absolute difference of gait energies for the l(1)-norm) to measure a dissimilarity, large intrasubject differences derived from covariate conditions (e.g., large gait energies caused by carried objects vs. small gait energies caused by the background), may wash out subtle intersubject differences (e.g., the difference of middle-level gait energies derived from motion differences). We therefore introduce a metric on joint intensity to mitigate the large intrasubject differences as well as leverage the subtle intersubject differences. More specifically, we formulate the joint intensity and spatial metric learning in a unified framework and alternately optimize it by linear or ranking support vector machines. Experiments using the OU-ISIR treadmill data set B with the largest clothing variation and large population data set with bag, beta version containing carrying status in the wild demonstrate the effectiveness of the proposed method.

关键词： Measurement Gait recognition Robustness Clothing Histograms Support vector machines Probes

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Reinforcement Cutting-Agent Learning for Video Object Segmentation 31

Reinforcement Cutting-Agent Learning for Video Object Segmen...

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31st ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Han, Junwei Yang, Le Zhang, Dingwen Chang, Xiaojun Liang, Xiaodan Northwestern Polytech Univ Xian Shaanxi Peoples R China Xidian Univ Xian Shaanxi Peoples R China Carnegie Mellon Univ Pittsburgh PA 15213 USA

ISBN: (纸本)9781538664209

Video object segmentation is a fundamental yet challenging task in computer vision community. In this paper, we formulate this problem as a Markov Decision Process, where agents are learned to segment object regions under a deep reinforcement learning framework. Essentially, learning agents for segmentation is nontrivial as segmentation is a nearly continuous decision-making process, where the number of the involved agents (pixels or superpixels) and action steps from the seed (super) pixels to the whole object mask might be incredibly huge. To overcome this difficulty, this paper simplifies the learning of segmentation agents to the learning of a cutting-agent, which only has a limited number of action units and can converge in just a few action steps. The basic assumption is that object segmentation mainly relies on the interaction between object regions and their context. Thus, with an optimal object (box) region and context (box) region, we can obtain the desirable segmentation mask through further inference. Based on this assumption, we establish a novel reinforcement cutting-agent learning framework, where the cutting-agent consists of a cutting-policy network and a cutting-execution network. The former learns policies for deciding optimal object-context box pair, while the latter executes the cutting function based on the inferred object-context box pair. With the collaborative interaction between the two networks, our method can achieve the outperforming VOS performance on two public benchmarks, which demonstrates the rationality of our assumption as well as the effectiveness of the proposed learning framework.

关键词： Object segmentation Task analysis Search problems computer vision Decision making Tracking

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Human Action recognition with Extremities as Semantic Posture Representation

Human Action Recognition with Extremities as Semantic Postur...

引用

ieee-computer-Society conference on computer vision and pattern recognition Workshops

作者： Yu, Elden Aggarwal, J. K. Univ Texas Austin Comp & Vis Res Ctr Austin TX 78712 USA

ISBN: (纸本)9781424439942

In this paper, we present at? approach for human action recognition with extremities as a compact semantic posture representation. First, we develop a variable star skeleton representation (VSS) in order to accurately,find human extremities front contours. Earlier, Fujiyoshi and Lipton [7] proposed an image skeletonization technique with the center of mass as a single star for rapid motion analysis. Yu and Aggarwal [18] used the highest contour point as the second star in their application for fence climbing detection. We implement VSS and earlier algorithms from [7, 18], and compare their performance over a set of 1000 frames from 50 sequences of persons climbing fences to analyze the characteristic of each representation. Our results show that VSS performs the best. Second, we build feature vectors out of detected extremities for Hidden Markov Model (HMM) based human action recognition. On the data set of human climbing fences, we achieved excellent classification accuracy. On the publicly available Blank et al. [3] data set, our approach showed that using only extremities is sufficient to obtain comparable classification accuracy against other state-of-the-art performance. The advantage of our approach lies in the less time complexity with comparable classification accuracy.

关键词： Fences

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Deep Representation Learning on Long-tailed Data: A Learnable Embedding Augmentation Perspective

Deep Representation Learning on Long-tailed Data: A Learnabl...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Liu, Jialun Sun, Yifan Han, Chuchu Dou, Zhaopeng Li, Wenhui Jilin Univ Jilin Jilin Peoples R China Megvii Inc Beijing Peoples R China Huazhong Univ Sci & Technol Wuhan Peoples R China Tsinghua Univ Beijing Peoples R China

ISBN: (纸本)9781728171685

This paper considers learning deep features from long-tailed data. We observe that in the deep feature space, the head classes and the tail classes present different distribution patterns. The head classes have a relatively large spatial span, while the tail classes have a significantly small spatial span, due to the lack of intra-class diversity. This uneven distribution between head and tail classes distorts the overall feature space, which compromises the discriminative ability of the learned features. In response, we seek to expand the distribution of the tail classes during training, so as to alleviate the distortion of the feature space. To this end, we propose to augment each instance of the tail classes with certain disturbances in the deep feature space. With the augmentation, a specified feature vector becomes a set of probable features scattered around itself which is analogical to an atomic nucleus surrounded by the electron cloud. Intuitively, we name it as "feature cloud". The intra-class distribution of the feature cloud is learned from the head classes, and thus provides higher intra-class variation to the tail classes. Consequentially, it alleviates the distortion of the learned feature space, and improves deep representation learning on long tailed data. Extensive experimental evaluations on person re-identification and face recognition tasks confirm the effectiveness of our method.

关键词： Face recognition

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